Chapter CHAPTER 4. Basic Probability. Assessing Probability. Example of a priori probability

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1 Chapter CHAPTER 4. Basic Probability Basic Probability Concepts Probability the chance that an uncertain event will occur (always between 0 and 1) Impossible Event an event that has no chance of occurring (probability 0) Certain Event an event that is sure to occur (probability 1) Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 1 Assessing Probability There are three approaches to assessing the probability of an uncertain event: Assuming all outcomes are equally likely 1. a priori -- based on prior knowledge of the process X number of ways in which the event occurs probability of occurrence T total number of possibleoutcomes 2. empirical probability number of ways in which the event occurs probability of occurrence total number of possibleoutcomes 3. subjective probability based on a combination of an individual s past experience, personal opinion, and analysis of a particular situation Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 2 Example of a priori probability When randomly selecting a day from the year 2014 what is the probability the day is in January? X number of days in January Probabilit y of Day In January T total number of days in 2014 X T 31 days in January 365 days in Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 3

2 Chapter Example of empirical probability Find the probability of selecting a male taking statistics from the population described in the following table: Taking Stats Not Taking Stats Total Male Female Total Probability of male taking stats number of males taking stats total number of people Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 4 Subjective probability Subjective probability may differ from person to person A media development team assigns a 60% probability of success to its new ad campaign. The chief media officer of the company is less optimistic and assigns a 40% of success to the same campaign The assignment of a subjective probability is based on a person s experiences, opinions, and analysis of a particular situation Subjective probability is useful in situations when an empirical or a priori probability cannot be computed Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 5 Events Each possible outcome of a variable is an event. Simple event An event described by a single characteristic e.g., A day in January from all days in 2014 Joint event An event described by two or more characteristics e.g. A day in January that is also a Wednesday from all days in 2014 Complement of an event A (denoted A ) All events that are not part of event A e.g., All days from 2014 that are not in January Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 6

3 Chapter Sample Space The Sample Space is the collection of all possible events e.g. All 6 faces of a die: e.g. All 52 cards of a bridge deck: Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 7 Organizing & Visualizing Events Venn Diagram For All Days In 2014 Sample Space (All Days In 2014) Days That Are In January and Are Wednesdays January Days Wednesdays Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 8 Sample Space Organizing & Visualizing Events Contingency Tables -- For All Days in 2014 Decision Trees All Days In 2014 Jan. Not Jan. Total Wed Not Wed Total Total Number Of Sample Space Outcomes Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 9

4 Chapter Definition: Simple Probability Simple Probability refers to the probability of a simple event. ex. P(Jan.) ex. P(Wed.) Jan. Not Jan. Total Wed Not Wed P(Wed.) 52 / 365 Total P(Jan.) 31 / 365 Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 10 Definition: Joint Probability Joint Probability refers to the probability of an occurrence of two or more events (joint event). ex. P(Jan. and Wed.) ex. P(Not Jan. and Not Wed.) Jan. Not Jan. Total Wed Not Wed P(Not Jan. and Not Wed.) 286 / 365 Total P(Jan. and Wed.) 5 / 365 Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 11 Mutually Exclusive Events Mutually exclusive events Events that cannot occur simultaneously Example: Randomly choosing a day from 2014 A day in January; B day in February Events A and B are mutually exclusive Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 12

5 Chapter Collectively Exhaustive Events Collectively exhaustive events One of the events must occur The set of events covers the entire sample space Example: Randomly choose a day from 2014 A Weekday; B Weekend; C January; D Spring; Events A, B, C and D are collectively exhaustive (but not mutually exclusive a weekday can be in January or in Spring) Events A and B are collectively exhaustive and also mutually exclusive Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 13 Computing Joint and Marginal Probabilities The probability of a joint event, A and B: P (A and B) number of outcomes satisfying A and B total number of elementary outcomes Computing a marginal (or simple) probability: P(A) P(A and B1 ) + P(A and B2) + L+ P(A and Bk) Where B 1, B 2,, B k are k mutually exclusive and collectively exhaustive events Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 14 Joint Probability Example P(Jan. and Wed.) number of days that are in Jan. and are Wed. total number of days in Jan. Not Jan. Total Wed Not Wed Total Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 15

6 Chapter Marginal Probability Example Jan. Not Jan. Total Wed Not Wed Total Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 16 Marginal & Joint Probabilities In A Contingency Table Event A 1 Event B 1 B 2 Total P(A 1 and B 1 ) P(A 1 and B 2 ) P(A 1 ) A 2 P(A 2 and B 1 ) P(A 2 and B 2 ) P(A 2 ) Total P(B 1 ) P(B 2 ) 1 Joint Probabilities Marginal (Simple) Probabilities Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 17 Probability Summary So Far Probability is the numerical measure of the likelihood that an event will occur The probability of any event must be between 0 and 1, inclusively 0 P(A) 1 For any event A The sum of the probabilities of all mutually exclusive and collectively exhaustive events is 1 P(A) + P(B) + P(C) 1 If A, B, and C are mutually exclusive and collectively exhaustive Certain Impossible Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 18

7 Chapter General Addition Rule General Addition Rule: P(A or B) P(A) + P(B) - P(A and B) If A and B are mutually exclusive, then P(A and B) 0, so the rule can be simplified: P(A or B) P(A) + P(B) For mutually exclusive events A and B Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 19 General Addition Rule Example P(Jan. or Wed.) P(Jan.) + P(Wed.) - P(Jan. and Wed.) 31/ /365-5/365 79/365 Jan. Not Jan. Total Wed Not Wed Total Don t count the five Wednesdays in January twice! Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 20 Computing Conditional Probabilities A conditional probability is the probability of one event, given that another event has occurred: P(A and B) P(A B) P(B) P(Aand B) P(B A) P(A) The conditional probability of A given that B has occurred The conditional probability of B given that A has occurred Where P(A and B) joint probability of A and B P(A) marginal or simple probability of A P(B) marginal or simple probability of B Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 21

8 Chapter Conditional Probability: Example Of the cars on a used car lot, 70% have air conditioning (AC) and 40% have a GPS. 20% of the cars have both. What is the probability that a car has a GPS, given that it has AC? i.e., we want to find P(GPS AC) Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 22 Conditional Probability: Example Of the cars on a used car lot, 70% have air conditioning (AC) and 40% have a GPS and 20% of the cars have both. GPS No GPS Total AC No AC Total P(GPS and AC) 0.2 P(GPS AC) P(AC) 0.7 Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 23 Conditional Probability: Example Given AC, we only consider the top row (70% of the cars). Of these, 20% have a GPS. 20% of 70% is about 28.57%. GPS No GPS Total AC No AC Total P(GPS and AC) 0.2 P(GPS AC) P(AC) 0.7 Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 24

9 Chapter Given AC or no AC: Using Decision Trees.2.7 P(AC and GPS) 0.2 All Cars Conditional Probabilities P(AC and GPS ) 0.5 P(AC and GPS) P(AC and GPS ) 0.1 Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 25 Given GPS or no GPS: Using Decision Trees.2.4 P(GPS and AC) 0.2 All Cars Conditional Probabilities P(GPS and AC ) 0.2 P(GPS and AC) P(GPS and AC ) 0.1 Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 26 Independence Two events are independent if and only if: P(A B) P(A) Events A and B are independent when the probability of one event is not affected by the fact that the other event has occurred Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 27

10 Chapter Multiplication Rules Multiplication rule for two events A and B: P(A and B) P(A B)P(B) Note: If A and B are independent, then and the multiplication rule simplifies to P(A and B) P(A)P(B) P(A B) P(A) Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 28 Marginal Probability Marginal probability for event A: P(A) P(A B1 )P(B1) + P(A B2)P(B2) + L + P(A Bk)P(Bk ) Where B 1, B 2,, B k are k mutually exclusive and collectively exhaustive events Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 29 Bayes Theorem Bayes Theorem is used to revise previously calculated probabilities based on new information. Developed by Thomas Bayes in the 18 th Century. It is an extension of conditional probability. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 30

11 Chapter Bayes Theorem (2) P(A B i)p(b i) P(B A) i P(A B )P(B ) + P(A B )P(B ) P(A B )P(B ) k k where: B i i th event of k mutually exclusive and collectively exhaustive events A new event that might impact P(B i ) Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 31 Bayes Theorem: Example A drilling company has estimated a 40% chance of striking oil for their new well. A detailed test has been scheduled for more information. Historically, 60% of successful wells have had detailed tests, and 20% of unsuccessful wells have had detailed tests. Given that this well has been scheduled for a detailed test, what is the probability that the well will be successful? Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 32 Bayes Theorem: Example Let S successful well U unsuccessful well P(S) 0.4, P(U) 0.6 (prior probabilities) Define the detailed test event as D Conditional probabilities: P(D S) 0.6 P(D U) 0.2 Goal is to find P(S D) Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 33

12 Chapter Bayes Theorem: Example Apply Bayes Theorem: P(S D) P(D S)P(S) P(D S)P(S) + P(D U)P(U) (0.6)(0.4) (0.6)(0.4) + (0.2)(0.6) So the revised probability of success, given that this well has been scheduled for a detailed test, is Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 34 Bayes Theorem Example Given the detailed test, the revised probability of a successful well has risen to from the original estimate of 0.4 Event Prior Prob. Conditional Prob. Joint Prob. Revised Prob. S (successful) (0.4)(0.6) / U (unsuccessful) (0.6)(0.2) / Sum 0.36 Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 35 Counting Rules Are Often Useful In Computing Probabilities In many cases, there are a large number of possible outcomes. Counting rules can be used in these cases to help compute probabilities. Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 36

13 Chapter Counting Rules Rules for counting the number of possible outcomes Counting Rule 1: If any one of k different mutually exclusive and collectively exhaustive events can occur on each of n trials, the number of possible outcomes is equal to Example k n If you roll a fair die 3 times then there are possible outcomes Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 37 Counting Rules Counting Rule 2: If there are k 1 events on the first trial, k 2 events on the second trial, and k n events on the n th trial, the number of possible outcomes is Example: (k 1 )(k 2 ) (k n ) You want to go to a park, eat at a restaurant, and see a movie. There are 3 parks, 4 restaurants, and 6 movie choices. How many different possible combinations are there? Answer: (3)(4)(6) 72 different possibilities Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 38 Counting Rules Counting Rule 3: The number of ways that n items can be arranged in order is Example: n! (n)(n 1) (1) You have five books to put on a bookshelf. How many different ways can these books be placed on the shelf? Answer: 5! (5)(4)(3)(2)(1) 120 different possibilities Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 39

14 Chapter Counting Rules Counting Rule 4: Permutations: The number of ways of arranging X objects selected from n objects in order is Example: You have five books and are going to put three on a bookshelf. How many different ways can the books be ordered on the bookshelf? Answer: n! (n X)! n P x - n! 5! 120 n P x 60 (n-x)! (5-3)! 2 different possibilities Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 40 Counting Rules Counting Rule 5: Combinations: The number of ways of selecting X objects from n objects, irrespective of order, is Example: You have five books and are going to select three are to read. How many different combinations are there, ignoring the order in which they are selected? Answer: n! X!(n X)! n C x - n! 5! X! (n-x)! 3!(5-3)! (6)(2) n C x different possibilities Copyright 2015, 2012, 2009 Pearson Education, Inc. Chapter 04, Slide 41